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Question 462 of 1,755

Exploratory Data Analysis →mediumMultiple ChoiceObjective-mapped

Why You Must Check Missingness Pattern Before Imputation

This MLS-C01 practice question tests your understanding of exploratory data analysis. Read the scenario carefully and evaluate each option against the stated constraints before committing to an answer. After answering, compare your reasoning against the explanation and wrong-answer breakdown below. Once you have made your selection, read the full explanation to reinforce the concept and understand why each distractor is designed to mislead on exam day.

A data scientist is working with a dataset that has missing values in 30% of rows for a categorical feature 'city'. Which EDA step should be performed before deciding on imputation?

A
Check if missingness is related to other features or random
Option A is correct because before deciding on imputation, you must investigate the pattern of missingness to determine if it is MCAR, MAR, or MNAR. This involves checking if missingness in 'city' is related to other features or random. Understanding the missing mechanism informs the appropriate imputation strategy.
B
Impute missing values with the mode of the column
Why wrong: Option B is wrong because imputing missing values with the mode is an imputation method, not a diagnostic step. Applying it without prior analysis of the missingness pattern risks introducing bias and may lead to incorrect conclusions.
C
Drop all rows with missing values
Why wrong: This is wrong because dropping rows with missing values is a data cleaning action, not an initial EDA step. It may be considered after analyzing the missingness pattern, but is not the first step.
D
Encode the city feature using label encoding
Why wrong: This is wrong because label encoding is a categorical data transformation, not related to handling missing values. It does not address the missing data issue.

Full breakdown with real-world context →

Answer choices

Why each option matters

Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.

Correct answer & explanation

✓

Check if missingness is related to other features or random

Before deciding on imputation for the 'city' feature, the first exploratory data analysis (EDA) step is to investigate the pattern of missingness. Option A is correct because you must determine whether the missing data are Missing Completely at Random (MCAR), Missing at Random (MAR), or Missing Not at Random (MNAR). This involves checking if missingness in 'city' is related to other features or is random. Understanding the missing mechanism informs the appropriate imputation strategy. Option B (impute with mode) is an imputation method, not a diagnostic step; applying it without prior analysis risks introducing bias. Option C (drop rows) may be valid only if missingness is MCAR and the amount of data loss is acceptable, but it should not be the first step. Option D (label encoding) transforms categorical data and does not address missing values.

Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.

Answer analysis

Option-by-option breakdown

For each option: why learners choose it and why it is or isn't the right answer here.

✓
Check if missingness is related to other features or random
Why this is correct
Option A is correct because before deciding on imputation, you must investigate the pattern of missingness to determine if it is MCAR, MAR, or MNAR. This involves checking if missingness in 'city' is related to other features or random. Understanding the missing mechanism informs the appropriate imputation strategy.
Related concept
Read the scenario before looking for a memorised answer.
✗
Impute missing values with the mode of the column
Why it's wrong here
Option B is wrong because imputing missing values with the mode is an imputation method, not a diagnostic step. Applying it without prior analysis of the missingness pattern risks introducing bias and may lead to incorrect conclusions.
✗
Drop all rows with missing values
Why it's wrong here
This is wrong because dropping rows with missing values is a data cleaning action, not an initial EDA step. It may be considered after analyzing the missingness pattern, but is not the first step.
✗
Encode the city feature using label encoding
Why it's wrong here
This is wrong because label encoding is a categorical data transformation, not related to handling missing values. It does not address the missing data issue.

Common exam traps

Common exam trap: answer the scenario, not the keyword

Many certification questions include familiar terms but test a specific constraint. Read the exact wording before choosing an answer that is generally true but wrong for this case.

Detailed technical explanation

How to think about this question

This question should be treated as a scenario, not a definition check. Identify the problem, the constraint and the best action. Then compare each option against those facts.

KKey Concepts to Remember

Read the scenario before looking for a memorised answer.
Find the constraint that changes the correct option.
Eliminate answers that are true in general but not in this case.
Use explanations to understand the rule behind the answer.

TExam Day Tips

Underline the problem statement mentally.
Watch for words such as best, first, most likely and least administrative effort.
Review why wrong options are wrong, not only why the correct option is correct.

Key takeaway

Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.

Real-world example

How this comes up in practice

A cloud solutions architect for a retail company is evaluating services for a new workload. The correct answer here reflects best practice for the specific scenario described — not a general cloud recommendation. Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option. Cloud exam questions reward reading the constraint carefully: the same technology can be right or wrong depending on the use case.

What to study next

Got this wrong? Here's your next step.

Identify which MLS-C01 exam domain this question belongs to, then review the specific concept being tested. Practise related questions in that domain and focus on understanding why each wrong answer is tempting — not just why the correct answer is right.

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FAQ

Questions learners often ask

What does this MLS-C01 question test?

Exploratory Data Analysis — This question tests Exploratory Data Analysis — Read the scenario before looking for a memorised answer..

What is the correct answer to this question?

The correct answer is: Check if missingness is related to other features or random — Before deciding on imputation for the 'city' feature, the first exploratory data analysis (EDA) step is to investigate the pattern of missingness. Option A is correct because you must determine whether the missing data are Missing Completely at Random (MCAR), Missing at Random (MAR), or Missing Not at Random (MNAR). This involves checking if missingness in 'city' is related to other features or is random. Understanding the missing mechanism informs the appropriate imputation strategy. Option B (impute with mode) is an imputation method, not a diagnostic step; applying it without prior analysis risks introducing bias. Option C (drop rows) may be valid only if missingness is MCAR and the amount of data loss is acceptable, but it should not be the first step. Option D (label encoding) transforms categorical data and does not address missing values.

What should I do if I get this MLS-C01 question wrong?

What is the key concept behind this question?

Read the scenario before looking for a memorised answer.

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This MLS-C01 practice question is part of Courseiva's free Amazon Web Services certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the MLS-C01 exam.